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Journal articles on the topic 'Structured data'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Kavita, Ahuja, and N.N.Jani. "A STUDY OF TRADITIONAL DATA ANALYSIS AND SENSOR DATA ANALYTICS." International Journal of Information Sciences and Techniques (IJIST) 6, no. 1/2 (2016): 185–90. https://doi.org/10.5281/zenodo.7743171.

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The growth of smart and intelligent devices known as sensors generate large amount of data. These generated data over a time span takes such a large volume which is designated as big data. The data structure of repository holds unstructured data. The traditional data analytics methods well developed and used widely to analyze structured data and to limit extend the semi-structured data which involves additional processing over heads. The similar methods used to analyze unstructured data are different because of distributed computing approach where as there is a possibility of centralized proce

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2

Rath, Shonal, and Hardik Agrawal. "Decentralized Computational Structure for the Selection of Dispersed and Non - Structured Data Proposal." International Journal of Science and Research (IJSR) 10, no. 9 (2021): 815–21. https://doi.org/10.21275/sr21916144223.

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3

Li, Shujie, Liang Li, Ruiying Geng, et al. "Unifying Structured Data as Graph for Data-to-Text Pre-Training." Transactions of the Association for Computational Linguistics 12 (2024): 210–28. http://dx.doi.org/10.1162/tacl_a_00641.

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Abstract Data-to-text (D2T) generation aims to transform structured data into natural language text. Data-to-text pre-training has proved to be powerful in enhancing D2T generation and yields impressive performance. However, previous pre-training methods either oversimplified structured data into a sequence without considering input structures or designed training objectives tailored for a specific data structure (e.g., table or knowledge graph). In this paper, we unify different types of structured data (i.e., table, key-value data, knowledge graph) into the graph format and cast different D2

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4

Fasihuddin, Mirza. "Integrating with Various Data Sources and Formats, Including Structured, Semi-Structured, and Unstructured Data." Journal of Scientific and Engineering Research 8, no. 2 (2021): 263–68. https://doi.org/10.5281/zenodo.11216190.

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The increasing availability and importance of data in various formats have led to the necessity for efficient integration methods to extract meaningful insights. This academic journal explores the challenges and solutions associated with integrating data from multiple sources, including structured, semi-structured, and unstructured data. The study aims to provide an overview of the techniques and tools available to businesses and researchers for effectively integrating diverse data types, enabling better decision-making and improving overall data-driven processes.

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5

Kunz, Donald L., and A. Stewart Hopkins. "Structured data in structural analysis software." Computers & Structures 26, no. 6 (1987): 965–78. http://dx.doi.org/10.1016/0045-7949(87)90114-3.

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6

Roukema, J., A. M. van Ginneken, M. de Wilde, J. van der Lei, and R. K. Los. "Are Structured Data Structured Identically?" Methods of Information in Medicine 44, no. 05 (2005): 631–38. http://dx.doi.org/10.1055/s-0038-1634019.

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Summary Objective: OpenSDE is an application that supports structured recording of narrative patient data to enable use of the data in both clinical practice and clinical research. Reliability and accuracy of collected data are essential for subsequent data use. In this study we analyze the uniformity of data entered with OpenSDE. Our objective is to obtain insight into the consensus and differences of recorded data. Methods: Three pediatricians transcribed 20 paper patient records using OpenSDE. The transcribed records were compared and all recorded findings were classified into one of six ca

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7

Shin, Kilho, and Dave Shepard. "Morphism-Based Learning for Structured Data." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5767–75. http://dx.doi.org/10.1609/aaai.v34i04.6033.

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In mathematics, morphism is a term that indicates structure-preserving mappings between mathematical structures of the same type. Linear transformations for linear spaces, homomorphisms for algebraic structures and continuous functions for topological spaces are examples. Many data researched in machine learning, on the other hand, can include mathematical structures in them. Strings are totally ordered sets, and trees can be understood not only as graphs but also as partially ordered sets with respect to an ancestor-to-descendent order and semigroups with respect to the binary operation to de

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8

Zhang, Y. "Open-access and Structured Data in Drug Discovery." Biomedical Data Journal 01, no. 1 (2015): 39–41. http://dx.doi.org/10.11610/bmdj.01107.

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9

Coolen, Henny. "Measurement and Analysis of Less Structured Data in Housing Research." Open House International 32, no. 3 (2007): 55–65. http://dx.doi.org/10.1108/ohi-03-2007-b0007.

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Two ideal types of data can be distinguished in housing research: structured and less-structured data. Questionnaires and official statistics are examples of structured data, while less-structured data arise for instance from open interviews and documents. Structured data are sometimes labelled quantitative, while less-structured data are called qualitative. In this paper structured and less-structured data are considered from the perspective of measurement and analysis. Structured data arise when the researcher has an a priori category system or measurement scale available for collecting the

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10

Malewski, Stefan, Michael Greenberg, and Éric Tanter. "Gradually structured data." Proceedings of the ACM on Programming Languages 5, OOPSLA (2021): 1–29. http://dx.doi.org/10.1145/3485503.

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Dynamically-typed languages offer easy interaction with ad hoc data such as JSON and S-expressions; statically-typed languages offer powerful tools for working with structured data, notably algebraic datatypes , which are a core feature of typed languages both functional and otherwise. Gradual typing aims to reconcile dynamic and static typing smoothly. The gradual typing literature has extensively focused on the computational aspect of types, such as type safety, effects, noninterference, or parametricity, but the application of graduality to data structuring mechanisms has been much less exp

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11

Da San Martino, Giovanni, and Alessandro Sperduti. "Mining Structured Data." IEEE Computational Intelligence Magazine 5, no. 1 (2010): 42–49. http://dx.doi.org/10.1109/mci.2009.935308.

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12

Sorber, Laurent, Marc Van Barel, and Lieven De Lathauwer. "Structured Data Fusion." IEEE Journal of Selected Topics in Signal Processing 9, no. 4 (2015): 586–600. http://dx.doi.org/10.1109/jstsp.2015.2400415.

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13

Butaboev, Makhammadjon Tuychiyevich, and Moydinjon Abdurakhmanovich Arziyev. "ESSENCE OF BIG DATA." International Journal Of Management And Economics Fundamental 4, no. 4 (2024): 40–49. http://dx.doi.org/10.37547/ijmef/volume04issue04-06.

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In this article, we consider the rapidly developing sphere in the digital economy -big data, which in the modern world allows governments, businesses and social structures to turn tons of heterogeneous information into structured knowledge. The experience of developing the big data science in the leading countries of the world is discussed and, using their example, the possibility of implementing big data in Uzbekistan is considered.

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14

Wu, P. D., Y. Yin, C. M. Li, and X. L. Liu. "AGGREGATION IN LAND-COVER DATA GENERALIZATION CONSIDERING SPATIAL STRUCTURE CHARACTERISTICS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W9 (September 30, 2019): 111–18. http://dx.doi.org/10.5194/isprs-annals-iv-4-w9-111-2019.

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Abstract. Aggregation is an important operation for the generalization of land-cover data. However, current research often entails aggregation on a global perspective, which is not conducive to capturing the spatial characteristics of geographic objects with significant spatial structures, i.e., structured geographic objects. Hence this paper proposes an area aggregation method that can maintain the boundary characteristics of the structured geographic objects. First, we identify the structured geographic objects based on the description parameters of the spatial structure. Second, a Miter-typ

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15

Korn, Flip, Barna Saha, Divesh Srivastava, and Shanshan Ying. "On repairing structural problems in semi-structured data." Proceedings of the VLDB Endowment 6, no. 9 (2013): 601–12. http://dx.doi.org/10.14778/2536360.2536361.

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16

Monakhova, Tatyana Vyacheslavovna. "XML-structured data protection." SPIIRAS Proceedings 2, no. 25 (2014): 182. http://dx.doi.org/10.15622/sp.25.8.

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17

Florescu, Daniela. "Managing Semi-Structured Data." Queue 3, no. 8 (2005): 18–24. http://dx.doi.org/10.1145/1103822.1103832.

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18

Balbi, Simona. "Analysis of Structured Data." Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 68, no. 1 (2000): 84–85. http://dx.doi.org/10.1177/075910630006800129.

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19

Schulze, Philipp, Benjamin Unger, Christopher Beattie, and Serkan Gugercin. "Data-driven structured realization." Linear Algebra and its Applications 537 (January 2018): 250–86. http://dx.doi.org/10.1016/j.laa.2017.09.030.

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20

Mastorci, F., and G. Iervasi. "The Need for Open-access, Structured Data in Endocrine Research." Biomedical Data Journal 01, no. 1 (2015): 33–35. http://dx.doi.org/10.11610/bmdj.01105.

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21

Pingitore, A., and C. Carpeggiani. "The Need for Open-access Structured Data in Cardiology Research." Biomedical Data Journal 01, no. 1 (2015): 36–38. http://dx.doi.org/10.11610/bmdj.01106.

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22

Stroo-Moredo, Elena, and Marnix Krikke. "Improving the Reuse of Design Data during the Tender Phase." Journal of Ship Production and Design 31, no. 02 (2015): 67–78. http://dx.doi.org/10.5957/jspd.2015.31.2.67.

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This article presents a methodology to improve the reuse of design data in the tender phase. This methodology consists of the implementation of two novel structures: a Functional Breakdown Structure and a System Breakdown Structure. The first provides a tool to capture that key data, which is currently missing, for the reuse at an early stage of design. The second structure ensures that the design data are structured, documented, and easy accessible and retrievable for new tenders. The two structures are linked to ensure the traceability and reusability of design data for new tenders.

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23

Singh, Shashi Pal, Ajai Kumar, Rachna Awasthi, Neetu Yadav, and Shikha Jain. "Intelligent Bilingual Data Extraction and Rebuilding Using Data Mining for Big Data." Journal of Computational and Theoretical Nanoscience 17, no. 1 (2020): 513–18. http://dx.doi.org/10.1166/jctn.2020.8699.

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In today’s World there exists various source of data in various formats (file formats), different structure, different types and etc. which is a hug collection of unstructured over the internet or social media. This gives rise to categorization of data as unstructured, semi structured and structured data. Data that exist in irregular manner without any particular schema are referred as unstructured data which is very difficult to process as it consists of irregularities and ambiguities. So, we are focused on Intelligent Processing Unit which converts unstructured big data into intelligent mean

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24

Tran, Thanh, Gunter Ladwig, and Sebastian Rudolph. "Managing Structured and Semistructured RDF Data Using Structure Indexes." IEEE Transactions on Knowledge and Data Engineering 25, no. 9 (2013): 2076–89. http://dx.doi.org/10.1109/tkde.2012.134.

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25

Usharani, B. "Mapping the Semi-Structured Data to the Structured Data for Inverted Index Compression." International Journal of Database Theory and Application 10, no. 1 (2017): 235–44. http://dx.doi.org/10.14257/ijdta.2017.10.1.22.

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26

Grigalis, Tomas, and Antanas Čenys. "Unsupervised Structured Data Extraction from Template-generated Web Pages." JUCS - Journal of Universal Computer Science 20, no. (2) (2014): 169–92. https://doi.org/10.3217/jucs-020-02-0169.

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This paper studies structured data extraction from template-generated Web pages. Such pages contain most of structured data on the Web. Extracted structured data can be later integrated and reused in very big range of applications, such as price comparison portals, business intelligence tools, various mashups and etc. It encourages industry and academics to seek automatic solutions. To tackle the problem of automatic structured Web data extraction we present a new approach - structured data extraction based on clustering visually similar Web page elements. Our method called ClustVX combines vi

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27

Rachapudi, Nikitha, Lakshmipathy Ganesh, Abinaya Sekar, Anand K. S, and Rajkumar Sakthibalan. "Discovery of Structured Data Using Unsupervised Spatial Clustering and Human Supervision." International Journal of Machine Learning and Computing 9, no. 5 (2019): 586–91. http://dx.doi.org/10.18178/ijmlc.2019.9.5.844.

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28

Daci, Genti, and Megi Shyle. "Improving data integrity and performance of cryptographic log structured file systems." Applied Technologies and Innovations 5, no. 2 (2011): 1–10. http://dx.doi.org/10.15208/ati.2011.8.

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29

Bunevicius, A. "The Need for Open-access, Structured Data in Clinical Brain Research." Biomedical Data Journal 01, no. 1 (2015): 27–32. http://dx.doi.org/10.11610/bmdj.01104.

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30

K Gautam, Leena. "Natural Language Processing - Based Structured Data Extraction from Unstructured Clinical Notes." International Journal of Science and Research (IJSR) 13, no. 4 (2024): 1541–44. http://dx.doi.org/10.21275/sr24422134801.

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31

tom Wörden, Henrik, Florian Spreckelsen, Stefan Luther, Ulrich Parlitz, and Alexander Schlemmer. "Mapping Hierarchical File Structures to Semantic Data Models for Efficient Data Integration into Research Data Management Systems." Data 9, no. 2 (2024): 24. http://dx.doi.org/10.3390/data9020024.

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Although other methods exist to store and manage data in modern information technology, the standard solution is file systems. Therefore, keeping well-organized file structures and file system layouts can be key to a sustainable research data management infrastructure. However, file structures alone lack several important capabilities for FAIR data management: the two most significant being insufficient visualization of data and inadequate possibilities for searching and obtaining an overview. Research data management systems (RDMSs) can fill this gap, but many do not support the simultaneous

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32

Goel, Alexander K., Walter Scott Campbell, and Richard Moldwin. "Structured Data Capture for Oncology." JCO Clinical Cancer Informatics, no. 5 (February 2021): 194–201. http://dx.doi.org/10.1200/cci.20.00103.

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Lack of interoperability is one of the greatest challenges facing healthcare informatics. Recent interoperability efforts have focused primarily on data transmission and generally ignore data capture standardization. Structured Data Capture (SDC) is an open-source technical framework that enables the capture and exchange of standardized and structured data in interoperable data entry forms (DEFs) at the point of care. Some of SDC’s primary use cases concern complex oncology data such as anatomic pathology, biomarkers, and clinical oncology data collection and reporting. Its interoperability go

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33

Goodenough, Sally. "Structured data, standards, and indexes." Indexer: The International Journal of Indexing 31, no. 4 (2013): 133–37. http://dx.doi.org/10.3828/indexer.2013.45.

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34

Singh, Mohini. "Structured Data and Electronic Filings." CFA Institute Magazine 27, no. 2 (2016): 62. http://dx.doi.org/10.2469/cfm.v27.n2.22.

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35

Badouel, Eric, Loïc Hélouët, and Christophe Morvan. "Petri Nets with Structured Data." Fundamenta Informaticae 146, no. 1 (2016): 35–82. http://dx.doi.org/10.3233/fi-2016-1375.

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36

Plewis, I., and G. Raab. "Editorial: Modelling structured categorical data." Journal of the Royal Statistical Society: Series A (Statistics in Society) 162, no. 3 (1999): 269–71. http://dx.doi.org/10.1111/1467-985x.00134.

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37

Cafarella, Michael J., Alon Halevy, and Jayant Madhavan. "Structured data on the web." Communications of the ACM 54, no. 2 (2011): 72–79. http://dx.doi.org/10.1145/1897816.1897839.

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38

Henkel, Maurice, and Bram Stieltjes. "Structured Data Acquisition in Oncology." Oncology 98, Suppl. 6 (2019): 423–29. http://dx.doi.org/10.1159/000504259.

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39

Husain, Moula, Somashekhar Patil, B. Indira, S. M. Meena, and D. G. Narayan. "Structured Approach of Designing Data Structure and Algorithms Laboratory Experiments." Journal of Engineering Education Transformations 29, no. 2 (2015): 83. http://dx.doi.org/10.16920/jeet/2015/v29i2/83072.

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40

Hannemann, V. "Structured multigrid agglomeration on a data structure for unstructured meshes." International Journal for Numerical Methods in Fluids 40, no. 3-4 (2002): 361–68. http://dx.doi.org/10.1002/fld.292.

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41

Liu, Feng Hua. "Research on the Data Model and the Approaches to Data Mining in the Semi-Structured Data." Applied Mechanics and Materials 513-517 (February 2014): 663–66. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.663.

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As an important form of Internet data, semi-structured data in data mining is an important fist conditions. And the data mining was designed to find and extract large database in the implied information of value. This paper first introduced the half structured data concept characteristic, based on the data from each of the half structural said, the data model two half-and-half structured data model are introduced, finally summarizes semi-structured data model and the relationship between the data model before difference [1].

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42

Skillicorn, D. "Structured Parallel Computation in Structured Documents." JUCS - Journal of Universal Computer Science 3, no. (1) (1997): 42–68. https://doi.org/10.3217/jucs-003-01-0042.

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Document archives contain large amounts of data to which sophisticated queries are applied. The size of archives and the complexity of evaluating queries makes the use of parallelism attractive. The use of semantically-based markup such as SGML makes it possible to represent documents and document archives as data types. We present a theory of trees and tree homomorphisms, modelling structured text archives and operations on them, from which it can be seen that: many apparently unrelated tree operations are homomorphisms, homomorphisms can be described in a simple parameterised way that gives

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43

Siradj, Yahdi, Kiki Maulana Adhinugraha, and Eric Pardede. "Towards Structured Gaze Data Classification: The Gaze Data Clustering Taxonomy (GCT)." Multimodal Technologies and Interaction 9, no. 5 (2025): 42. https://doi.org/10.3390/mti9050042.

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Gaze data analysis plays a crucial role in understanding human visual attention and behaviour. However, raw gaze data is often noisy and lacks inherent structure, making interpretation challenging. Therefore, preprocessing techniques such as classification are essential to extract meaningful patterns and improve the reliability of gaze-based analysis. This study introduces the Gaze Data Clustering Taxonomy (GCT), a novel approach that categorises gaze data into structured clusters to improve its reliability and interpretability. GCT classifies gaze data based on cluster count, target presence,

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44

Seema, Rai, and Sharma Ashok. "Research Perspective on Security Based Algorithm in Big Data Concepts." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 2138–43. https://doi.org/10.35940/ijeat.C5407.029320.

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Providing a robust security for large data is one in all the first concern for most of the researchers. This paper makes an attempt to uncover all the protection solutions associated with unstructured, structured and semi structured data. also, the main aim of this paper is to cover the information related with the several encryption algorithms used to provide confidentiality, integrity, privacy and data silos. Different algorithmic program and tools play an efficient role in playacting significant analysis on huge volume, variety of big data.

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45

Hassan, Dr Syed Tabrez. "Use of Machine Learning Classifiers on Structured Vs Unstructured Data." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29771.

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This study investigates the efficacy of machine learning classifiers on structured versus unstructured data. Structured data, organized in predefined formats, enables conventional analysis. Conversely, unstructured data, lacking predefined structures, presents challenges in processing. We assess classifier performance across diverse datasets, focusing on accuracy, efficiency, and adaptability. Results reveal distinct classifier behaviors: structured data favors traditional algorithms, while unstructured data necessitates advanced techniques such as natural language processing and deep learning

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46

Takehara, Daisuke, and Kei Kobayashi. "Representing Hierarchical Structured Data Using Cone Embedding." Mathematics 11, no. 10 (2023): 2294. http://dx.doi.org/10.3390/math11102294.

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Extracting hierarchical structure in graph data is becoming an important problem in fields such as natural language processing and developmental biology. Hierarchical structures can be extracted by embedding methods in non-Euclidean spaces, such as Poincaré embedding and Lorentz embedding, and it is now possible to learn efficient embedding by taking advantage of the structure of these spaces. In this study, we propose embedding into another type of metric space called a metric cone by learning an only one-dimensional coordinate variable added to the original vector space or a pre-trained embe

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47

Chen, Qun, Andrew Lim, Kian Win Ong, and Ji Qing Tang. "Indexing graph-structured XML data for efficient structural join operation." Data & Knowledge Engineering 58, no. 2 (2006): 159–79. http://dx.doi.org/10.1016/j.datak.2005.05.008.

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48

Praveen, Shagufta. "Conversion of Unstructured to Structured: A Solution Using Data Science and NOSQL." Revista Gestão Inovação e Tecnologias 11, no. 4 (2021): 1772–77. http://dx.doi.org/10.47059/revistageintec.v11i4.2235.

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49

Eisinger, Daniel, George Tsatsaronis, Alina Petrova, Efstathios Karanastasis, Vassiliki Andronikou, and Efthymios Chondrogiannis. "OSL Platform: A Link to Open-access Scientific Information and Structured Data." Biomedical Data Journal 01, no. 1 (2015): 52–54. http://dx.doi.org/10.11610/bmdj.01109.

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50

Kelkar, Bhagyashri A., and Dr S. F. Rodd. "A Review of Feature Selection Techniques for Clustering High Dimensional Structured Data." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (2016): 176–79. http://dx.doi.org/10.9756/bijsesc.8270.

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